SMS Spam Classifier

Abstract

Mobile communication is widely used for personal and professional purpose. spam SMS message have become a major problem, causing annoyance and security risks. These messages can lead to privacy issues and financial losses. The SMS Spam Classifier project aims to automatically detect and block such messages. It uses Machine Learning and NLP techniques for accurate classification. A naive bayes model is trained on SMS data after preprocessing text with tokenization, stop word removal, and TF-IDF vectorization. Users can access the system through a web interface to check messages and view spam statistics. The main goal is to provide a fast, reliable, and user- friendly solution to reduce spam SMS.